


"""simple docstring"""






import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_lowerCAmelCase							:    str					   =	{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}




class 							A_      (       nn.Module ):


  def __init__(			self:						Optional[int]							,__lowerCAmelCase:						Any    ):







     '''simple docstring'''







     super().__init__()
     _lowerCamelCase   :				Optional[Any]							=     torchvision.models.resnetaaa(pretrained=__lowerCAmelCase    )
     _lowerCamelCase   :				Union[str, Any]							=     list(model.children()    )[:-2]
     _lowerCamelCase   :				Optional[int]							=     nn.Sequential(*__lowerCAmelCase    )
     _lowerCamelCase   :				Dict							=     nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]    )






  def 							_lowercase					(			self:						Tuple							,__lowerCAmelCase:						int    ):







     '''simple docstring'''







     _lowerCamelCase   :				Dict							=     self.pool(self.model(__lowerCAmelCase    )    )
     _lowerCamelCase   :				Union[str, Any]							=     torch.flatten(__lowerCAmelCase							,start_dim=2    )
     _lowerCamelCase   :				Tuple							=     out.transpose(1							,2    ).contiguous()
     return out  # BxNx2048




class 							A_      (       _a ):


  def __init__(			self:						str							,__lowerCAmelCase:						Optional[int]							,__lowerCAmelCase:						Union[str, Any]							,__lowerCAmelCase:						Optional[int]							,__lowerCAmelCase:						Any							,__lowerCAmelCase:						int    ):







     '''simple docstring'''







     _lowerCamelCase   :				List[str]							=     [json.loads(__lowerCAmelCase    ) for l in open(__lowerCAmelCase    )]
     _lowerCamelCase   :				Any							=     os.path.dirname(__lowerCAmelCase    )
     _lowerCamelCase   :				Tuple							=     tokenizer
     _lowerCamelCase   :				Union[str, Any]							=     labels
     _lowerCamelCase   :				Dict							=     len(__lowerCAmelCase    )
     _lowerCamelCase   :				Any							=     max_seq_length

     _lowerCamelCase   :				List[Any]							=     transforms


  def __len__(			self:						List[Any]    ):







     '''simple docstring'''







     return len(self.data    )


  def __getitem__(			self:						Tuple							,__lowerCAmelCase:						List[Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				int							=     torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]							,add_special_tokens=__lowerCAmelCase    )    )
     _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase   :				Optional[int]							=     sentence[0], sentence[1:-1], sentence[-1]
     _lowerCamelCase   :				Optional[Any]							=     sentence[: self.max_seq_length]

     _lowerCamelCase   :				Any							=     torch.zeros(self.n_classes    )
     _lowerCamelCase   :				Union[str, Any]							=     1

     _lowerCamelCase   :				Optional[int]							=     Image.open(os.path.join(self.data_dir							,self.data[index]["img"]    )    ).convert("RGB"    )
     _lowerCamelCase   :				Optional[int]							=     self.transforms(__lowerCAmelCase    )

     return {
         "image_start_token": start_token,
         "image_end_token": end_token,
         "sentence": sentence,
         "image": image,
         "label": label,
     }






  def 							_lowercase					(			self:						Optional[int]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Any							=     Counter()
     for row in self.data:
        label_freqs.update(row["label"]    )
     return label_freqs







def        lowerCamelCase_(		_lowerCamelCase       )    ->     str:
   '''simple docstring'''





   _lowerCamelCase   :				List[Any]							=     [len(row["sentence"]       ) for row in batch]
   _lowerCamelCase,					_lowerCamelCase   :				Dict							=     len(_lowerCamelCase       ), max(_lowerCamelCase       )

   _lowerCamelCase   :				int							=     torch.zeros(_lowerCamelCase			,		_lowerCamelCase			,		dtype=torch.long       )
   _lowerCamelCase   :				Tuple							=     torch.zeros(_lowerCamelCase			,		_lowerCamelCase			,		dtype=torch.long       )

   for i_batch, (input_row, length) in enumerate(zip(_lowerCamelCase			,		_lowerCamelCase       )       ):
      _lowerCamelCase   :				Dict							=     input_row["sentence"]
      _lowerCamelCase   :				Optional[Any]							=     1

   _lowerCamelCase   :				List[str]							=     torch.stack([row["image"] for row in batch]       )
   _lowerCamelCase   :				List[Any]							=     torch.stack([row["label"] for row in batch]       )
   _lowerCamelCase   :				Union[str, Any]							=     torch.stack([row["image_start_token"] for row in batch]       )
   _lowerCamelCase   :				Dict							=     torch.stack([row["image_end_token"] for row in batch]       )

   return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor







def        lowerCamelCase_(		)    ->     int:
   '''simple docstring'''





   return [
       "Crime",
       "Drama",
       "Thriller",
       "Action",
       "Comedy",
       "Romance",
       "Documentary",
       "Short",
       "Mystery",
       "History",
       "Family",
       "Adventure",
       "Fantasy",
       "Sci-Fi",
       "Western",
       "Horror",
       "Sport",
       "War",
       "Music",
       "Musical",
       "Animation",
       "Biography",
       "Film-Noir",
   ]







def        lowerCamelCase_(		)    ->     Tuple:
   '''simple docstring'''





   return transforms.Compose(
       [
           transforms.Resize(256       ),
           transforms.CenterCrop(224       ),
           transforms.ToTensor(),
           transforms.Normalize(
               mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7]			,		std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9]			,		),
       ]       )



'''simple docstring'''


import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


UpperCAmelCase_						:  Optional[Any]				  =						16
UpperCAmelCase_						:  List[str]				  =						32







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__ = 16				, SCREAMING_SNAKE_CASE__ = "bert-base-cased"	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Dict							= AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			List[str]							= load_dataset("""glue"""				, """mrpc"""	)

				def tokenize_function(SCREAMING_SNAKE_CASE__	):
								# max_length=None => use the model max length (it's actually the default)
								_SCREAMING_SNAKE_CASE					:			List[str]							= tokenizer(examples["""sentence1"""]				, examples["""sentence2"""]				, truncation=SCREAMING_SNAKE_CASE__				, max_length=SCREAMING_SNAKE_CASE__	)
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				_SCREAMING_SNAKE_CASE					:			str							= datasets.map(
				    SCREAMING_SNAKE_CASE__				, batched=SCREAMING_SNAKE_CASE__				, remove_columns=["""idx""", """sentence1""", """sentence2"""]				, load_from_cache_file=SCREAMING_SNAKE_CASE__	)

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				_SCREAMING_SNAKE_CASE					:			Tuple							= tokenized_datasets.rename_column("""label"""				, """labels"""	)

				def collate_fn(SCREAMING_SNAKE_CASE__	):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(SCREAMING_SNAKE_CASE__				, padding="""max_length"""				, max_length=128				, return_tensors="""pt"""	)
								return tokenizer.pad(SCREAMING_SNAKE_CASE__				, padding="""longest"""				, return_tensors="""pt"""	)

				# Instantiate dataloaders.
				_SCREAMING_SNAKE_CASE					:			int							= DataLoader(
				    tokenized_datasets["""train"""]				, shuffle=SCREAMING_SNAKE_CASE__				, collate_fn=SCREAMING_SNAKE_CASE__				, batch_size=SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Any							= DataLoader(
				    tokenized_datasets["""validation"""]				, shuffle=SCREAMING_SNAKE_CASE__				, collate_fn=SCREAMING_SNAKE_CASE__				, batch_size=SCREAMING_SNAKE_CASE__	)

				return train_dataloader, eval_dataloader







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				model.eval()
				_SCREAMING_SNAKE_CASE					:			Dict							= 0
				for step, batch in enumerate(SCREAMING_SNAKE_CASE__	):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device	)
								with torch.no_grad():
												_SCREAMING_SNAKE_CASE					:			Tuple							= model(**SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= outputs.logits.argmax(dim=-1	)
								# It is slightly faster to call this once, than multiple times
								_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= accelerator.gather(
								    (predictions, batch["""labels"""])	)  # If we are in a multiprocess environment, the last batch has duplicates
								if accelerator.use_distributed:
												if step == len(SCREAMING_SNAKE_CASE__	) - 1:
																_SCREAMING_SNAKE_CASE					:			List[str]							= predictions[: len(eval_dataloader.dataset	) - samples_seen]
																_SCREAMING_SNAKE_CASE					:			List[Any]							= references[: len(eval_dataloader.dataset	) - samples_seen]
												else:
																samples_seen += references.shape[0]
								metric.add_batch(
								    predictions=SCREAMING_SNAKE_CASE__				, references=SCREAMING_SNAKE_CASE__				, )

				_SCREAMING_SNAKE_CASE					:			str							= metric.compute()
				return eval_metric["accuracy"]







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				_SCREAMING_SNAKE_CASE					:			Optional[int]							= config["""lr"""]
				_SCREAMING_SNAKE_CASE					:			Any							= int(config["""num_epochs"""]	)
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= int(config["""seed"""]	)
				_SCREAMING_SNAKE_CASE					:			Tuple							= int(config["""batch_size"""]	)
				_SCREAMING_SNAKE_CASE					:			List[str]							= args.model_name_or_path

				set_seed(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Optional[Any]							= get_dataloaders(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__				, return_dict=SCREAMING_SNAKE_CASE__	)

				# Instantiate optimizer
				_SCREAMING_SNAKE_CASE					:			List[str]							= (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				_SCREAMING_SNAKE_CASE					:			Any							= optimizer_cls(params=model.parameters()				, lr=SCREAMING_SNAKE_CASE__	)

				if accelerator.state.deepspeed_plugin is not None:
								_SCREAMING_SNAKE_CASE					:			List[Any]							= accelerator.state.deepspeed_plugin.deepspeed_config[
								    """gradient_accumulation_steps"""
								]
				else:
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 1
				_SCREAMING_SNAKE_CASE					:			int							= (len(SCREAMING_SNAKE_CASE__	) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								_SCREAMING_SNAKE_CASE					:			List[Any]							= get_linear_schedule_with_warmup(
								    optimizer=SCREAMING_SNAKE_CASE__				, num_warmup_steps=0				, num_training_steps=SCREAMING_SNAKE_CASE__				, )
				else:
								_SCREAMING_SNAKE_CASE					:			Any							= DummyScheduler(SCREAMING_SNAKE_CASE__				, total_num_steps=SCREAMING_SNAKE_CASE__				, warmup_num_steps=0	)

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Tuple							= accelerator.prepare(
				    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

				# We need to keep track of how many total steps we have iterated over
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= 0
				# We also need to keep track of the stating epoch so files are named properly
				_SCREAMING_SNAKE_CASE					:			str							= 0
				_SCREAMING_SNAKE_CASE					:			Tuple							= evaluate.load("""glue"""				, """mrpc"""	)
				_SCREAMING_SNAKE_CASE					:			int							= num_epochs

				if args.partial_train_epoch is not None:
								_SCREAMING_SNAKE_CASE					:			Dict							= args.partial_train_epoch

				if args.resume_from_checkpoint:
								accelerator.load_state(args.resume_from_checkpoint	)
								_SCREAMING_SNAKE_CASE					:			Any							= args.resume_from_checkpoint.split("""epoch_"""	)[1]
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= """"""
								for char in epoch_string:
												if char.isdigit():
																state_epoch_num += char
												else:
																break
								_SCREAMING_SNAKE_CASE					:			int							= int(SCREAMING_SNAKE_CASE__	) + 1
								_SCREAMING_SNAKE_CASE					:			List[str]							= evaluation_loop(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
								accelerator.print("""resumed checkpoint performance:"""				, SCREAMING_SNAKE_CASE__	)
								accelerator.print("""resumed checkpoint's scheduler's lr:"""				, lr_scheduler.get_lr()[0]	)
								accelerator.print("""resumed optimizers's lr:"""				, optimizer.param_groups[0]["""lr"""]	)
								with open(os.path.join(args.output_dir				, f"""state_{starting_epoch-1}.json"""	)				, """r"""	) as f:
												_SCREAMING_SNAKE_CASE					:			Any							= json.load(SCREAMING_SNAKE_CASE__	)
												assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
												assert (
												    resumed_state["lr"] == lr_scheduler.get_lr()[0]
												), "Scheduler learning rate mismatch, loading from checkpoint failed"
												assert (
												    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
												), "Optimizer learning rate mismatch, loading from checkpoint failed"
												assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
												return

    # Now we train the model
				_SCREAMING_SNAKE_CASE					:			int							= {}
				for epoch in range(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):
								model.train()
								for step, batch in enumerate(SCREAMING_SNAKE_CASE__	):
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= model(**SCREAMING_SNAKE_CASE__	)
												_SCREAMING_SNAKE_CASE					:			Optional[Any]							= outputs.loss
												_SCREAMING_SNAKE_CASE					:			int							= loss / gradient_accumulation_steps
												accelerator.backward(SCREAMING_SNAKE_CASE__	)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1
								_SCREAMING_SNAKE_CASE					:			int							= f"""epoch_{epoch}"""
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= os.path.join(args.output_dir				, SCREAMING_SNAKE_CASE__	)
								accelerator.save_state(SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= evaluation_loop(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			Dict							= accuracy
								_SCREAMING_SNAKE_CASE					:			Any							= lr_scheduler.get_lr()[0]
								_SCREAMING_SNAKE_CASE					:			Any							= optimizer.param_groups[0]["""lr"""]
								_SCREAMING_SNAKE_CASE					:			Dict							= epoch
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= overall_step
								accelerator.print(f"""epoch {epoch}:"""				, SCREAMING_SNAKE_CASE__	)

								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												with open(os.path.join(args.output_dir				, f"""state_{epoch}.json"""	)				, """w"""	) as f:
																json.dump(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)







def      snake_case_  (    ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			List[str]							= argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""	)
				parser.add_argument(
				    """--model_name_or_path"""				, type=SCREAMING_SNAKE_CASE__				, default="""bert-base-cased"""				, help="""Path to pretrained model or model identifier from huggingface.co/models."""				, required=SCREAMING_SNAKE_CASE__				, )
				parser.add_argument(
				    """--output_dir"""				, type=SCREAMING_SNAKE_CASE__				, default="""."""				, help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""				, )
				parser.add_argument(
				    """--resume_from_checkpoint"""				, type=SCREAMING_SNAKE_CASE__				, default=SCREAMING_SNAKE_CASE__				, help="""If the training should continue from a checkpoint folder."""				, )
				parser.add_argument(
				    """--partial_train_epoch"""				, type=SCREAMING_SNAKE_CASE__				, default=SCREAMING_SNAKE_CASE__				, help="""If passed, the training will stop after this number of epochs."""				, )
				parser.add_argument(
				    """--num_epochs"""				, type=SCREAMING_SNAKE_CASE__				, default=2				, help="""Number of train epochs."""				, )
				_SCREAMING_SNAKE_CASE					:			str							= parser.parse_args()
				_SCREAMING_SNAKE_CASE					:			int							= {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

				training_function(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
				main()




